OpenRaiser/Intern-Atlas huggingface dataset:
Harmonization and the Worst Scanner Syndrome
Venue: arXiv cs.LG 2021
Year: 2021

We show that for a wide class of harmonization/domain-invariance schemes several undesirable properties are unavoidable. If a predictive machine is made invariant to a set of domains, the accuracy of the output predictions (as measured by mutual information) is limited by the domain with the least amount of information to begin with. If a real label value is highly informative about the source domain, it cannot be accurately predicted by an invariant predictor. These results are simple and intuitive, but we believe that it is beneficial to state them for medical imaging harmonization.
Data-Driven Protection Levels for Camera and 3D Map-based Safe Urban Localization
Venue: arXiv cs.RO 2021
Year: 2021

Reliably assessing the error in an estimated vehicle position is integral for ensuring the vehicle's safety in urban environments. Many existing approaches use GNSS measurements to characterize protection levels (PLs) as probabilistic upper bounds on the position error. However, GNSS signals might be reflected or blocked in urban environments, and thus additional sensor modalities need to be considered to determine PLs. In this paper, we propose a novel approach for computing PLs by matching camera image measurements to a LiDAR-based 3D map of the environment. We specify a Gaussian mixture model probability distribution of position error using deep neural network-based data-driven models and statistical outlier weighting techniques. From the probability distribution, we compute the PLs by evaluating the position error bound using numerical line-search methods. Through experimental validation with real-world data, we demonstrate that the PLs computed from our method are reliable bounds on the position error in urban environments.
Reliable GNSS Localization Against Multiple Faults Using a Particle Filter Framework
Venue: arXiv cs.RO 2021
Year: 2021

For reliable operation on urban roads, navigation using the Global Navigation Satellite System (GNSS) requires both accurately estimating the positioning detail from GNSS pseudorange measurements and determining when the estimated position is safe to use, or available. However, multiple GNSS measurements in urban environments contain biases, or faults, due to signal reflection and blockage from nearby buildings which are difficult to mitigate for estimating the position and availability. This paper proposes a novel particle filter-based framework that employs a Gaussian Mixture Model (GMM) likelihood of GNSS measurements to robustly estimate the position of a navigating vehicle under multiple measurement faults. Using the probability distribution tracked by the filter and the designed GMM likelihood, we measure the accuracy and the risk associated with localization and determine the availability of the navigation system at each time instant. Through experiments conducted on challenging simulated and real urban driving scenarios, we show that our method achieves small horizontal positioning errors compared to existing filter-based state estimation techniques when multiple GNSS measurements contain faults. Furthermore, we verify using several simulations that our method determines system availability with smaller probability of false alarms and integrity risk than the existing particle filter-based integrity monitoring approach.
GridTracer: Automatic Mapping of Power Grids using Deep Learning and Overhead Imagery
Venue: arXiv cs.CV 2021
Year: 2021

Energy system information valuable for electricity access planning such as the locations and connectivity of electricity transmission and distribution towers, termed the power grid, is often incomplete, outdated, or altogether unavailable. Furthermore, conventional means for collecting this information is costly and limited. We propose to automatically map the grid in overhead remotely sensed imagery using deep learning. Towards this goal, we develop and publicly-release a large dataset ($263km^2$) of overhead imagery with ground truth for the power grid, to our knowledge this is the first dataset of its kind in the public domain. Additionally, we propose scoring metrics and baseline algorithms for two grid mapping tasks: (1) tower recognition and (2) power line interconnection (i.e., estimating a graph representation of the grid). We hope the availability of the training data, scoring metrics, and baselines will facilitate rapid progress on this important problem to help decision-makers address the energy needs of societies around the world.
Free Lunch for Few-shot Learning: Distribution Calibration
Venue: arXiv cs.LG 2021
Year: 2021

Learning from a limited number of samples is challenging since the learned model can easily become overfitted based on the biased distribution formed by only a few training examples. In this paper, we calibrate the distribution of these few-sample classes by transferring statistics from the classes with sufficient examples, then an adequate number of examples can be sampled from the calibrated distribution to expand the inputs to the classifier. We assume every dimension in the feature representation follows a Gaussian distribution so that the mean and the variance of the distribution can borrow from that of similar classes whose statistics are better estimated with an adequate number of samples. Our method can be built on top of off-the-shelf pretrained feature extractors and classification models without extra parameters. We show that a simple logistic regression classifier trained using the features sampled from our calibrated distribution can outperform the state-of-the-art accuracy on two datasets (~5% improvement on miniImageNet compared to the next best). The visualization of these generated features demonstrates that our calibrated distribution is an accurate estimation.
Community Detection in Blockchain Social Networks
Venue: arXiv cs.SI 2021
Year: 2021

In this work, we consider community detection in blockchain networks. We specifically take the Bitcoin network and Ethereum network as two examples, where community detection serves in different ways. For the Bitcoin network, we modify the traditional community detection method and apply it to the transaction social network to cluster users with similar characteristics. For the Ethereum network, on the other hand, we define a bipartite social graph based on the smart contract transactions. A novel community detection algorithm which is designed for low-rank signals on graph can help find users' communities based on user-token subscription. Based on these results, two strategies are devised to deliver on-chain advertisements to those users in the same community. We implement the proposed algorithms on real data. By adopting the modified clustering algorithm, the community results in the Bitcoin network is basically consistent with the ground-truth of betting site community which has been announced to the public. At the meanwhile, we run the proposed strategy on real Ethereum data, visualize the results and implement an advertisement delivery on the Ropsten test net.
Blind Optimal User Association in Small-Cell Networks
Venue: arXiv cs.NI 2021
Year: 2021

We learn optimal user association policies for traffic from different locations to Access Points(APs), in the presence of unknown dynamic traffic demand. We aim at minimizing a broad family of $α$-fair cost functions that express various objectives in load assignment in the wireless downlink, such as total load or total delay minimization. Finding an optimal user association policy in dynamic environments is challenging because traffic demand fluctuations over time are non-stationary and difficult to characterize statistically, which obstructs the computation of cost-efficient associations. Assuming arbitrary traffic patterns over time, we formulate the problem of online learning of optimal user association policies using the Online Convex Optimization (OCO) framework. We introduce a periodic benchmark for OCO problems that generalizes state-of-the-art benchmarks. We exploit inherent properties of the online user association problem and propose PerOnE, a simple online learning scheme that dynamically adapts the association policy to arbitrary traffic demand variations. We compare PerOnE against our periodic benchmark and prove that it enjoys the no-regret property, with additional sublinear dependence of the network size. To the best of our knowledge, this is the first work that introduces a periodic benchmark for OCO problems and a no-regret algorithm for the online user association problem. Our theoretical findings are validated through results on a real-trace dataset.
Multi-objective Search of Robust Neural Architectures against Multiple Types of Adversarial Attacks
Venue: arXiv cs.LG 2021
Year: 2021

Many existing deep learning models are vulnerable to adversarial examples that are imperceptible to humans. To address this issue, various methods have been proposed to design network architectures that are robust to one particular type of adversarial attacks. It is practically impossible, however, to predict beforehand which type of attacks a machine learn model may suffer from. To address this challenge, we propose to search for deep neural architectures that are robust to five types of well-known adversarial attacks using a multi-objective evolutionary algorithm. To reduce the computational cost, a normalized error rate of a randomly chosen attack is calculated as the robustness for each newly generated neural architecture at each generation. All non-dominated network architectures obtained by the proposed method are then fully trained against randomly chosen adversarial attacks and tested on two widely used datasets. Our experimental results demonstrate the superiority of optimized neural architectures found by the proposed approach over state-of-the-art networks that are widely used in the literature in terms of the classification accuracy under different adversarial attacks.
Intrusion Detection Systems for Smart Home IoT Devices: Experimental Comparison Study
Venue: arXiv cs.CR 2021
Year: 2021

Smart homes are one of the most promising applications of the emerging Internet of Things (IoT) technology. With the growing number of IoT related devices such as smart thermostats, smart fridges, smart speaker, smart light bulbs and smart locks, smart homes promise to make our lives easier and more comfortable. However, the increased deployment of such smart devices brings an increase in potential security risks and home privacy breaches. In order to overcome such risks, Intrusion Detection Systems are presented as pertinent tools that can provide network-level protection for smart devices deployed in home environments. These systems monitor the network activities of the smart home-connected de-vices and focus on alerting suspicious or malicious activity. They also can deal with detected abnormal activities by hindering the impostors in accessing the victim devices. However, the employment of such systems in the context of a smart home can be challenging due to the devices hardware limitations, which may restrict their ability to counter the existing and emerging attack vectors. Therefore, this paper proposes an experimental comparison between the widely used open-source NIDSs namely Snort, Suricata and Bro IDS to find the most appropriate one for smart homes in term of detection accuracy and resources consumption including CP and memory utilization. Experimental Results show that Suricata is the best performing NIDS for smart homes
Overlap-Minimization Scheduling Strategy for Data Transmission in VANET
Venue: arXiv cs.NI 2021
Year: 2021

The vehicular ad-hoc network (VANET) based on dedicated short-range communication (DSRC) is a distributed communication system, in which all the nodes share the wireless channel with carrier sense multiple access/collision avoid (CSMA/CA) protocol. However, the competition and backoff mechanisms of CSMA/CA often bring additional delays and data packet collisions, which may hardly meet the QoS requirements in terms of delay and packets delivery ratio (PDR). Moreover, because of the distribution nature of security information in broadcast mode, the sender cannot know whether the receivers have received the information successfully. Similarly, this problem also exists in no-acknowledge (non-ACK) transmissions of VANET. Therefore, the probability of packet collisions should be considered in broadcast or non-ACK working modes. This paper presents a connection-level scheduling algorithm overlaid on CSMA/CA to schedule the start sending time of each transmission. By converting the object of reducing collision probability to minimizing the overlap of transmission durations of connections, the probability of backoff-activation can be greatly decreased. Then the delay and the probability of packet collisions can also be decreased. Numerical simulations have been conducted in our unified platform containing SUMO, Veins and Omnet++. The result shows that the proposed algorithm can effectively improve the PDR and reduce the packets collision in VANET.
Big Data application in congestion detection and classification using Apache spark
Venue: arXiv cs.DC 2021
Year: 2021

With the era of big data, an explosive amount of information is now available. This enormous increase of Big Data in both academia and industry requires large-scale data processing systems. A large body of research is behind optimizing Spark's performance to make it state of the art, a fast and general data processing system. Many science and engineering fields have advanced with Big Data analytics, such as Biology, finance, and transportation. Intelligent transportation systems (ITS) gain popularity and direct benefit from the richness of information. The objective is to improve the safety and management of transportation networks by reducing congestion and incidents. The first step toward the goal is better understanding, modeling, and detecting congestion across a network efficiently and effectively. In this study, we introduce an efficient congestion detection model. The underlying network consists of 3017 segments in I-35, I-80, I-29, and I-380 freeways with an overall length of 1570 miles and averaged (0.4-0.6) miles per segment. The result of congestion detection shows the proposed method is 90% accurate while has reduced computation time by 99.88%.
PL2: Towards Predictable Low Latency in Rack-Scale Networks
Venue: arXiv cs.NI 2021
Year: 2021

High performance rack-scale offerings package disaggregated pools of compute, memory and storage hardware in a single rack to run diverse workloads with varying requirements, including applications that need low and predictable latency. The intra-rack network is typically high speed Ethernet, which can suffer from congestion leading to packet drops and may not satisfy the stringent tail latency requirements for some workloads (including remote memory/storage accesses). In this paper, we design a Predictable Low Latency(PL2) network architecture for rack-scale systems with Ethernet as interconnecting fabric. PL2 leverages programmable Ethernet switches to carefully schedule packets such that they incur no loss with NIC and switch queues maintained at small, near-zero levels. In our 100 Gbps rack-prototype, PL2 keeps 99th-percentile memcached RPC latencies under 60us even when the RPCs compete with extreme offered-loads of 400%, without losing traffic. Network transfers for a machine learning training task complete 30% faster than a receiver-driven scheme implementation modeled after Homa (222ms vs 321ms 99%ile latency per iteration).
ConE: A Concurrent Edit Detection Tool for Large Scale Software Development
Venue: arXiv cs.SE 2021
Year: 2021

Modern, complex software systems are being continuously extended and adjusted. The developers responsible for this may come from different teams or organizations, and may be distributed over the world. This may make it difficult to keep track of what other developers are doing, which may result in multiple developers concurrently editing the same code areas. This, in turn, may lead to hard-to-merge changes or even merge conflicts, logical bugs that are difficult to detect, duplication of work, and wasted developer productivity. To address this, we explore the extent of this problem in the pull request based software development model. We study half a year of changes made to six large repositories in Microsoft in which at least 1,000 pull requests are created each month. We find that files concurrently edited in different pull requests are more likely to introduce bugs. Motivated by these findings, we design, implement, and deploy a service named ConE (Concurrent Edit Detector) that proactively detects pull requests containing concurrent edits, to help mitigate the problems caused by them. ConE has been designed to scale, and to minimize false alarms while still flagging relevant concurrently edited files. Key concepts of ConE include the detection of the Extent of Overlap between pull requests, and the identification of Rarely Concurrently Edited Files. To evaluate ConE, we report on its operational deployment on 234 repositories inside Microsoft. ConE assessed 26,000 pull requests and made 775 recommendations about conflicting changes, which were rated as useful in over 70% (554) of the cases. From interviews with 48 users we learned that they believed ConE would save time in conflict resolution and avoiding duplicate work, and that over 90% intend to keep using the service on a daily basis.
Deep-Mobility: A Deep Learning Approach for an Efficient and Reliable 5G Handover
Venue: arXiv cs.LG 2021
Year: 2021

5G cellular networks are being deployed all over the world and this architecture supports ultra-dense network (UDN) deployment. Small cells have a very important role in providing 5G connectivity to the end users. Exponential increases in devices, data and network demands make it mandatory for the service providers to manage handovers better, to cater to the services that a user desire. In contrast to any traditional handover improvement scheme, we develop a 'Deep-Mobility' model by implementing a deep learning neural network (DLNN) to manage network mobility, utilizing in-network deep learning and prediction. We use network key performance indicators (KPIs) to train our model to analyze network traffic and handover requirements. In this method, RF signal conditions are continuously observed and tracked using deep learning neural networks such as the Recurrent neural network (RNN) or Long Short-Term Memory network (LSTM) and system level inputs are also considered in conjunction, to take a collective decision for a handover. We can study multiple parameters and interactions between system events along with the user mobility, which would then trigger a handoff in any given scenario. Here, we show the fundamental modeling approach and demonstrate usefulness of our model while investigating impacts and sensitivities of certain KPIs from the user equipment (UE) and network side.
Membership Inference Attack on Graph Neural Networks
Venue: arXiv cs.LG 2021
Year: 2021

Graph Neural Networks (GNNs), which generalize traditional deep neural networks on graph data, have achieved state-of-the-art performance on several graph analytical tasks. We focus on how trained GNN models could leak information about the \emph{member} nodes that they were trained on. We introduce two realistic settings for performing a membership inference (MI) attack on GNNs. While choosing the simplest possible attack model that utilizes the posteriors of the trained model (black-box access), we thoroughly analyze the properties of GNNs and the datasets which dictate the differences in their robustness towards MI attack. While in traditional machine learning models, overfitting is considered the main cause of such leakage, we show that in GNNs the additional structural information is the major contributing factor. We support our findings by extensive experiments on four representative GNN models. To prevent MI attacks on GNN, we propose two effective defenses that significantly decreases the attacker's inference by up to 60% without degradation to the target model's performance. Our code is available at https://github.com/iyempissy/rebMIGraph.
Disentangling Observed Causal Effects from Latent Confounders using Method of Moments
Venue: arXiv cs.LG 2021
Year: 2021

Discovering the complete set of causal relations among a group of variables is a challenging unsupervised learning problem. Often, this challenge is compounded by the fact that there are latent or hidden confounders. When only observational data is available, the problem is ill-posed, i.e. the causal relationships are non-identifiable unless strong modeling assumptions are made. When interventions are available, we provide guarantees on identifiability and learnability under mild assumptions. We assume a linear structural equation model (SEM) with independent latent factors and directed acyclic graph (DAG) relationships among the observables. Since the latent variable inference is based on independent component analysis (ICA), we call this model SEM-ICA. We use the method of moments principle to establish model identifiability. We develop efficient algorithms based on coupled tensor decomposition with linear constraints to obtain scalable and guaranteed solutions. Thus, we provide a principled approach to tackling the joint problem of causal discovery and latent variable inference.
The Complexity of Bicriteria Tree-Depth
Venue: arXiv cs.DS 2021
Year: 2021

The tree-depth problem can be seen as finding an elimination tree of minimum height for a given input graph $G$. We introduce a bicriteria generalization in which additionally the width of the elimination tree needs to be bounded by some input integer $b$. We are interested in the case when $G$ is the line graph of a tree, proving that the problem is NP-hard and obtaining a polynomial-time additive $2b$-approximation algorithm. This particular class of graphs received significant attention in the past, mainly due to a number of potential applications, e.g. in parallel assembly of modular products, or parallel query processing in relational databases, as well as purely combinatorial applications, including searching in tree-like partial orders (which in turn generalizes binary search on sorted data).
Zero-touch Continuous Network Slicing Control via Scalable Actor-Critic Learning
Venue: arXiv cs.NI 2021
Year: 2021

Artificial intelligence (AI)-driven zero-touch network slicing is envisaged as a promising cutting-edge technology to harness the full potential of heterogeneous 5G and beyond 5G (B5G) communication systems and enable the automation of demand-aware resource management and orchestration (MANO). In this paper, we tackle the issue of B5G radio access network (RAN) joint slice admission control and resource allocation according to proposed slice-enabling cell-free massive multiple-input multiple-output (mMIMO) setup by invoking a continuous deep reinforcement learning (DRL) method. We present a novel Actor-Critic-based network slicing approach called, prioritized twin delayed distributional deep deterministic policy gradient (D-TD3)}. The paper defines and corroborates via extensive experimental results a zero-touch network slicing scheme with a multi-objective approach where the central server learns continuously to accumulate the knowledge learned in the past to solve future problems and re-configure computing resources autonomously while minimizing latency, energy consumption, and virtual network function (VNF) instantiation cost for each slice. Moreover, we pursue a state-action return distribution learning approach with the proposed replay policy and reward-penalty mechanisms. Finally, we present numerical results to showcase the gain of the adopted multi-objective strategy and verify the performance in terms of achieved slice admission rate, latency, energy, CPU utilization, and time efficiency.